code: def test_one_sided_selection_multiclass():
      """Check the validity of the fitted attributes `estimators_`."""
      X, y = make_classification(
          n_samples=1_000,
          n_classes=4,
          weights=[0.1, 0.2, 0.2, 0.5],
          n_clusters_per_class=1,
          random_state=0,
      )
      oss = OneSidedSelection(random_state=RND_SEED)
      oss.fit_resample(X, y)
  
      assert len(oss.estimators_) == len(oss.sampling_strategy_)
      other_classes = []
      for est in oss.estimators_:
          assert est.classes_[0] == 0  # minority class
          assert est.classes_[1] in {1, 2, 3}  # other classes
          other_classes.append(est.classes_[1])
      assert len(set(other_classes)) == len(other_classes)
  
file_path: imbalanced-learn/imblearn/under_sampling/_prototype_selection/tests/test_one_sided_selection.py
imports: import numpy
  import pytest
  from sklearn.datasets import make_classification
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.utils._testing import assert_array_equal
  from imblearn.under_sampling import OneSidedSelection
label_desc: Use image node_id 4 for calling a global function with example usage: test_one_sided_selection_multiclass() without return types